Overview

Dataset statistics

Number of variables13
Number of observations6497
Missing cells0
Missing cells (%)0.0%
Duplicate rows992
Duplicate rows (%)15.3%
Total size in memory660.0 KiB
Average record size in memory104.0 B

Variable types

Numeric11
Categorical2

Alerts

Dataset has 992 (15.3%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with typeHigh correlation
volatile acidity is highly overall correlated with typeHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
type is highly overall correlated with fixed acidity and 3 other fieldsHigh correlation
quality is highly imbalanced (76.8%)Imbalance
citric acid has 151 (2.3%) zerosZeros

Reproduction

Analysis started2023-03-02 16:22:52.535717
Analysis finished2023-03-02 16:22:59.936482
Duration7.4 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2153071
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:22:59.976808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2964338
Coefficient of variation (CV)0.17967825
Kurtosis5.0611607
Mean7.2153071
Median Absolute Deviation (MAD)0.6
Skewness1.7232896
Sum46877.85
Variance1.6807405
MonotonicityNot monotonic
2023-03-02T10:23:00.035456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 354
 
5.4%
6.6 327
 
5.0%
6.4 305
 
4.7%
7 282
 
4.3%
6.9 279
 
4.3%
7.2 273
 
4.2%
6.7 264
 
4.1%
7.1 257
 
4.0%
6.5 242
 
3.7%
7.4 238
 
3.7%
Other values (96) 3676
56.6%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
< 0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.1%
4.9 8
 
0.1%
5 30
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 2
< 0.1%
15 2
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct187
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.339666
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.096654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.16463647
Coefficient of variation (CV)0.48470107
Kurtosis2.8253724
Mean0.339666
Median Absolute Deviation (MAD)0.08
Skewness1.4950965
Sum2206.81
Variance0.027105169
MonotonicityNot monotonic
2023-03-02T10:23:00.155543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 286
 
4.4%
0.24 266
 
4.1%
0.26 256
 
3.9%
0.25 238
 
3.7%
0.22 235
 
3.6%
0.27 232
 
3.6%
0.23 221
 
3.4%
0.2 217
 
3.3%
0.3 214
 
3.3%
0.32 205
 
3.2%
Other values (177) 4127
63.5%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 6
 
0.1%
0.11 13
 
0.2%
0.115 3
 
< 0.1%
0.12 37
0.6%
0.125 3
 
< 0.1%
0.13 44
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

Distinct89
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31863322
Minimum0
Maximum1.66
Zeros151
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.220656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.14531786
Coefficient of variation (CV)0.45606628
Kurtosis2.3972392
Mean0.31863322
Median Absolute Deviation (MAD)0.07
Skewness0.47173067
Sum2070.16
Variance0.021117282
MonotonicityNot monotonic
2023-03-02T10:23:00.282620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 337
 
5.2%
0.28 301
 
4.6%
0.32 289
 
4.4%
0.49 283
 
4.4%
0.26 257
 
4.0%
0.34 249
 
3.8%
0.29 244
 
3.8%
0.27 236
 
3.6%
0.24 232
 
3.6%
0.31 230
 
3.5%
Other values (79) 3839
59.1%
ValueCountFrequency (%)
0 151
2.3%
0.01 40
 
0.6%
0.02 56
 
0.9%
0.03 32
 
0.5%
0.04 41
 
0.6%
0.05 25
 
0.4%
0.06 30
 
0.5%
0.07 34
 
0.5%
0.08 37
 
0.6%
0.09 42
 
0.6%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 2
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct316
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4432353
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.343741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7578037
Coefficient of variation (CV)0.87407644
Kurtosis4.3592719
Mean5.4432353
Median Absolute Deviation (MAD)1.7
Skewness1.4354043
Sum35364.7
Variance22.636696
MonotonicityNot monotonic
2023-03-02T10:23:00.402774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 235
 
3.6%
1.8 228
 
3.5%
1.6 223
 
3.4%
1.4 219
 
3.4%
1.2 195
 
3.0%
2.2 187
 
2.9%
2.1 179
 
2.8%
1.9 176
 
2.7%
1.7 175
 
2.7%
1.5 172
 
2.6%
Other values (306) 4508
69.4%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.4%
0.9 41
 
0.6%
0.95 4
 
0.1%
1 93
1.4%
1.05 1
 
< 0.1%
1.1 146
2.2%
1.15 3
 
< 0.1%
1.2 195
3.0%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

Distinct214
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056033862
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.464528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.035033601
Coefficient of variation (CV)0.62522197
Kurtosis50.898051
Mean0.056033862
Median Absolute Deviation (MAD)0.011
Skewness5.3998277
Sum364.052
Variance0.0012273532
MonotonicityNot monotonic
2023-03-02T10:23:00.520140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 206
 
3.2%
0.036 200
 
3.1%
0.042 187
 
2.9%
0.046 185
 
2.8%
0.05 182
 
2.8%
0.04 182
 
2.8%
0.048 182
 
2.8%
0.047 175
 
2.7%
0.045 174
 
2.7%
0.038 169
 
2.6%
Other values (204) 4655
71.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 10
0.2%
0.019 9
0.1%
0.02 16
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
< 0.1%
0.414 2
< 0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.525319
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.579507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.7494
Coefficient of variation (CV)0.58146483
Kurtosis7.9062381
Mean30.525319
Median Absolute Deviation (MAD)12
Skewness1.2200661
Sum198323
Variance315.04119
MonotonicityNot monotonic
2023-03-02T10:23:00.635031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 183
 
2.8%
6 170
 
2.6%
26 161
 
2.5%
15 157
 
2.4%
24 152
 
2.3%
31 152
 
2.3%
17 149
 
2.3%
34 146
 
2.2%
35 144
 
2.2%
23 142
 
2.2%
Other values (125) 4941
76.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.9%
4 52
 
0.8%
5 129
2.0%
5.5 1
 
< 0.1%
6 170
2.6%
7 96
1.5%
8 91
1.4%
9 91
1.4%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.74457
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.695411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.521855
Coefficient of variation (CV)0.48833265
Kurtosis-0.37166365
Mean115.74457
Median Absolute Deviation (MAD)39
Skewness-0.0011774782
Sum751992.5
Variance3194.72
MonotonicityNot monotonic
2023-03-02T10:23:00.752928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 72
 
1.1%
113 65
 
1.0%
117 57
 
0.9%
122 57
 
0.9%
128 56
 
0.9%
98 56
 
0.9%
124 56
 
0.9%
114 56
 
0.9%
118 55
 
0.8%
150 54
 
0.8%
Other values (266) 5913
91.0%
ValueCountFrequency (%)
6 3
 
< 0.1%
7 4
 
0.1%
8 14
 
0.2%
9 15
0.2%
10 28
0.4%
11 26
0.4%
12 29
0.4%
13 28
0.4%
14 33
0.5%
15 35
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99469663
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.813002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673
Coefficient of variation (CV)0.0030146609
Kurtosis6.606067
Mean0.99469663
Median Absolute Deviation (MAD)0.00231
Skewness0.50360173
Sum6462.544
Variance8.9920398 × 10-6
MonotonicityNot monotonic
2023-03-02T10:23:00.880665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9976 69
 
1.1%
0.9972 69
 
1.1%
0.998 64
 
1.0%
0.992 64
 
1.0%
0.9928 63
 
1.0%
0.9986 61
 
0.9%
0.9962 59
 
0.9%
0.9966 59
 
0.9%
0.9956 55
 
0.8%
0.9968 55
 
0.8%
Other values (988) 5879
90.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
 
< 0.1%
1.0103 2
< 0.1%
1.00369 2
< 0.1%
1.0032 1
 
< 0.1%
1.00315 3
< 0.1%
1.00295 2
< 0.1%
1.00289 1
 
< 0.1%
1.0026 2
< 0.1%
1.00242 2
< 0.1%
1.00241 1
 
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2185008
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:00.943099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607872
Coefficient of variation (CV)0.049957173
Kurtosis0.36765727
Mean3.2185008
Median Absolute Deviation (MAD)0.11
Skewness0.3868388
Sum20910.6
Variance0.025852524
MonotonicityNot monotonic
2023-03-02T10:23:01.007806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 200
 
3.1%
3.14 193
 
3.0%
3.22 185
 
2.8%
3.2 176
 
2.7%
3.15 170
 
2.6%
3.19 170
 
2.6%
3.18 168
 
2.6%
3.24 161
 
2.5%
3.1 154
 
2.4%
3.12 154
 
2.4%
Other values (98) 4766
73.4%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
 
< 0.1%
2.8 3
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.84 1
 
< 0.1%
2.85 9
0.1%
2.86 10
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53126828
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:01.184069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14880587
Coefficient of variation (CV)0.28009554
Kurtosis8.6536988
Mean0.53126828
Median Absolute Deviation (MAD)0.08
Skewness1.79727
Sum3451.65
Variance0.022143188
MonotonicityNot monotonic
2023-03-02T10:23:01.242867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 276
 
4.2%
0.46 243
 
3.7%
0.54 235
 
3.6%
0.44 232
 
3.6%
0.38 214
 
3.3%
0.48 208
 
3.2%
0.52 203
 
3.1%
0.49 197
 
3.0%
0.47 191
 
2.9%
0.45 190
 
2.9%
Other values (101) 4308
66.3%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 4
 
0.1%
0.27 13
 
0.2%
0.28 13
 
0.2%
0.29 16
 
0.2%
0.3 31
0.5%
0.31 35
0.5%
0.32 54
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
< 0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.491801
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-03-02T10:23:01.305206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1927117
Coefficient of variation (CV)0.11368037
Kurtosis-0.53168738
Mean10.491801
Median Absolute Deviation (MAD)0.9
Skewness0.56571773
Sum68165.23
Variance1.4225613
MonotonicityNot monotonic
2023-03-02T10:23:01.365455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 367
 
5.6%
9.4 332
 
5.1%
9.2 271
 
4.2%
10 229
 
3.5%
10.5 227
 
3.5%
11 217
 
3.3%
9 215
 
3.3%
9.8 214
 
3.3%
10.4 194
 
3.0%
9.3 193
 
3.0%
Other values (101) 4038
62.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 5
 
0.1%
8.5 10
 
0.2%
8.6 23
 
0.4%
8.7 80
 
1.2%
8.8 109
1.7%
8.9 95
1.5%
9 215
3.3%
9.05 1
 
< 0.1%
9.1 167
2.6%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 12
0.2%
13.9 3
 
< 0.1%
13.8 2
 
< 0.1%
13.7 7
0.1%
13.6 13
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
Legit
6251 
Fraud
 
246

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters32485
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLegit
2nd rowLegit
3rd rowLegit
4th rowLegit
5th rowLegit

Common Values

ValueCountFrequency (%)
Legit 6251
96.2%
Fraud 246
 
3.8%

Length

2023-03-02T10:23:01.420633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-02T10:23:01.470122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
legit 6251
96.2%
fraud 246
 
3.8%

Most occurring characters

ValueCountFrequency (%)
L 6251
19.2%
e 6251
19.2%
g 6251
19.2%
i 6251
19.2%
t 6251
19.2%
F 246
 
0.8%
r 246
 
0.8%
a 246
 
0.8%
u 246
 
0.8%
d 246
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25988
80.0%
Uppercase Letter 6497
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6251
24.1%
g 6251
24.1%
i 6251
24.1%
t 6251
24.1%
r 246
 
0.9%
a 246
 
0.9%
u 246
 
0.9%
d 246
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
L 6251
96.2%
F 246
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 32485
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 6251
19.2%
e 6251
19.2%
g 6251
19.2%
i 6251
19.2%
t 6251
19.2%
F 246
 
0.8%
r 246
 
0.8%
a 246
 
0.8%
u 246
 
0.8%
d 246
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 6251
19.2%
e 6251
19.2%
g 6251
19.2%
i 6251
19.2%
t 6251
19.2%
F 246
 
0.8%
r 246
 
0.8%
a 246
 
0.8%
u 246
 
0.8%
d 246
 
0.8%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
white
4898 
red
1599 

Length

Max length5
Median length5
Mean length4.5077728
Min length3

Characters and Unicode

Total characters29287
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowred
2nd rowred
3rd rowred
4th rowred
5th rowred

Common Values

ValueCountFrequency (%)
white 4898
75.4%
red 1599
 
24.6%

Length

2023-03-02T10:23:01.511830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-02T10:23:01.561780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
white 4898
75.4%
red 1599
 
24.6%

Most occurring characters

ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29287
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 29287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Interactions

2023-03-02T10:22:59.165739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:52.880855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.593487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.202114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.877404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.469918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.072128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.749275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.315243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.883711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.466362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.220183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:52.979517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.649134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.255124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.934579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.525591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.126920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.800385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.367307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.937106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.524772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.276269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.040940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.705114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.315991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.992829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.582695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.185267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.854114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.420527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.993849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.582602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.329435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.103350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.759604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.372612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.045556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.636414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.240201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.905698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.471850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.047215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.637137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.380957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.159700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.813764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.430155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.097168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.689677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.292681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.955084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.522973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.100490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.691091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.433100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.212755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.868536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.485676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.150113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.742986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.346735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.004603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.574296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.153192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.744612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.485389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.325198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.923180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.539363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.201878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.799611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.399283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.053157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.624897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.205438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.890106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.538968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.373581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.972586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.589776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.251306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.850587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.450920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.099935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.671152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.253307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.940545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.592501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.424704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.027384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.640823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.300740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.901567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.503812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.152613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.720587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.303251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.996407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.648193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.479669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.085908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.760985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.353552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.955419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.635744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.207302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.773106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.353672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.052630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.704484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:53.539070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.143978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:54.819263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:55.411701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.012649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:56.693430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.261872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:57.828399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:58.409048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-02T10:22:59.109295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-02T10:23:01.603443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
fixed acidity1.0000.2000.271-0.0320.356-0.260-0.2330.434-0.2500.220-0.1110.0030.504
volatile acidity0.2001.000-0.295-0.0640.416-0.366-0.3440.2610.1950.255-0.0240.1850.664
citric acid0.271-0.2951.0000.075-0.0740.1220.1590.066-0.2860.0370.0200.0980.424
residual sugar-0.032-0.0640.0751.000-0.0360.3880.4550.527-0.229-0.138-0.3290.0390.350
chlorides0.3560.416-0.074-0.0361.000-0.260-0.2680.5910.1640.370-0.4010.0510.765
free sulfur dioxide-0.260-0.3660.1220.388-0.2601.0000.7410.006-0.165-0.221-0.1860.1650.419
total sulfur dioxide-0.233-0.3440.1590.455-0.2680.7411.0000.062-0.243-0.257-0.3090.1130.800
density0.4340.2610.0660.5270.5910.0060.0621.0000.0120.275-0.6990.0370.322
pH-0.2500.195-0.286-0.2290.164-0.165-0.2430.0121.0000.2540.1400.0620.333
sulphates0.2200.2550.037-0.1380.370-0.221-0.2570.2750.2541.0000.0050.0460.472
alcohol-0.111-0.0240.020-0.329-0.401-0.186-0.309-0.6990.1400.0051.0000.0770.147
quality0.0030.1850.0980.0390.0510.1650.1130.0370.0620.0460.0771.0000.000
type0.5040.6640.4240.3500.7650.4190.8000.3220.3330.4720.1470.0001.000

Missing values

2023-03-02T10:22:59.786848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-02T10:22:59.888819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
07.40.700.001.90.07611.034.00.99783.510.569.4Legitred
17.80.880.002.60.09825.067.00.99683.200.689.8Legitred
27.80.760.042.30.09215.054.00.99703.260.659.8Legitred
311.20.280.561.90.07517.060.00.99803.160.589.8Legitred
47.40.700.001.90.07611.034.00.99783.510.569.4Legitred
57.40.660.001.80.07513.040.00.99783.510.569.4Legitred
67.90.600.061.60.06915.059.00.99643.300.469.4Legitred
77.30.650.001.20.06515.021.00.99463.390.4710.0Legitred
87.80.580.022.00.0739.018.00.99683.360.579.5Legitred
97.50.500.366.10.07117.0102.00.99783.350.8010.5Legitred
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
64876.80.2200.361.200.05238.0127.00.993303.040.549.2Legitwhite
64884.90.2350.2711.750.03034.0118.00.995403.070.509.4Legitwhite
64896.10.3400.292.200.03625.0100.00.989383.060.4411.8Legitwhite
64905.70.2100.320.900.03838.0121.00.990743.240.4610.6Legitwhite
64916.50.2300.381.300.03229.0112.00.992983.290.549.7Legitwhite
64926.20.2100.291.600.03924.092.00.991143.270.5011.2Legitwhite
64936.60.3200.368.000.04757.0168.00.994903.150.469.6Legitwhite
64946.50.2400.191.200.04130.0111.00.992542.990.469.4Legitwhite
64955.50.2900.301.100.02220.0110.00.988693.340.3812.8Legitwhite
64966.00.2100.380.800.02022.098.00.989413.260.3211.8Legitwhite

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype# duplicates
4607.00.150.2814.70.05129.0149.00.997922.960.399.0Legitwhite8
6227.30.190.2713.90.05745.0155.00.998072.940.418.8Legitwhite8
3606.80.180.3012.80.06219.0171.00.998083.000.529.0Legitwhite7
6617.40.160.3013.70.05633.0168.00.998252.900.448.7Legitwhite7
6607.40.160.2715.50.05025.0135.00.998402.900.438.7Legitwhite6
6647.40.190.3012.80.05348.5229.00.998603.140.499.1Legitwhite6
6657.40.190.3114.50.04539.0193.00.998603.100.509.2Legitwhite6
7287.60.200.3014.20.05653.0212.50.999003.140.468.9Legitwhite6
325.70.220.2016.00.04441.0113.00.998623.220.468.9Legitwhite5
1186.20.230.3617.20.03937.0130.00.999463.230.438.8Legitwhite5